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Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains

arXiv 2503.23060 ↗First posted 29 Mar 2025cs.LGcited by 0

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Abstract (arXiv)

The widespread adoption of digital services, along with the scale and complexity at which they operate, has made incidents in IT operations increasingly more likely, diverse, and impactful. This has led to the rapid development of a central aspect of "Artificial Intelligence for IT Operations" (AIOps), focusing on detecting anomalies in vast amounts of multivariate time series data generated by service entities. In this paper, we begin by introducing a unifying framework for benchmarking unsupervised anomaly detection (AD) methods, and highlight the problem of shifts in normal behaviors that can occur in practical AIOps scenarios. To tackle anomaly detection under domain shift, we then cast the problem in the framework of domain generalization and propose a novel approach, Domain-Invariant VAE for Anomaly Detection (DIVAD), to learn domain-invariant representations for unsupervised anomaly detection. Our evaluation results using the Exathlon benchmark show that the two main DIVAD variants significantly outperform the best unsupervised AD method in maximum performance, with 20% and 15% improvements in maximum peak F1-scores, respectively. Evaluation using the Application Server Dataset further demonstrates the broader applicability of our domain generalization methods.

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References · 22

  1. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  2. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  3. 2020Learning to Learn Single Domain Generalizationcited by 295
  4. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  5. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  6. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  7. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 181
  8. 2019Domain Generalization Using a Mixture of Multiple Latent Domainscited by 170
  9. 2020Efficient Domain Generalization via Common-Specific Low-Rank Decompositioncited by 134
  10. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  11. 2017Stabilizing Training of Generative Adversarial Networks through Regularizationcited by 118
  12. 2019DIVA: Domain Invariant Variational Autoencoderscited by 114
10 more
  1. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  2. 2023Domain Adaptation for Time Series Under Feature and Label Shiftscited by 60
  3. 2018Resampled Priors for Variational Autoencoderscited by 56
  4. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  5. 2019Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalizationcited by 43
  6. 2020Multi-Loss Weighting with Coefficient of Variationscited by 36
  7. 2018Distribution Matching in Variational Inferencecited by 35
  8. 2022Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniquescited by 18
  9. 2022Sintel: A Machine Learning Framework to Extract Insights from Signalscited by 16
  10. 2017Gauging Variational Inferencecited by 2

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